Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Downloading mnist: 9.92MB [00:00, 34.4MB/s]                            
Extracting mnist: 100%|██████████| 60.0K/60.0K [00:08<00:00, 6.98KFile/s] 
Downloading celeba: 1.44GB [03:40, 6.53MB/s]                                
Extracting celeba...

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7efe1d8555c0>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7efe1d780550>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function

    inputs_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='input_real') 
    inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')
    learning_rate = tf.placeholder(tf.float32, name='learning_rate')
    
    
    
    return inputs_real, inputs_z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
alpha = 0.1
dropout = 0.5
In [7]:
def leaky_relu(l):
    return tf.maximum(alpha * l, l)
In [8]:
def discriminator(images, reuse=False):
    with tf.variable_scope('discriminator', reuse=reuse):
        # TODO: Implement Function
        l1 = tf.layers.conv2d(images, 16, 7,strides=2, padding='same')
        l1 = tf.layers.batch_normalization(l1, training=True)
        relu1 = leaky_relu(l1)

        l2 = tf.layers.conv2d(l1, 32,5,strides=2, padding='same')
        l2 = tf.layers.batch_normalization(l2, training=True)
        relu2 = leaky_relu(l2)

        l3 = tf.layers.conv2d(l2, 64,3,strides=2, padding='same')
        l3 = tf.layers.batch_normalization(l3, training=True)
        relu3 = leaky_relu(l3)
        
        l4 = tf.layers.conv2d(l3, 128,1,strides=1, padding='same')
        l4 = tf.layers.batch_normalization(l4, training=True)
        relu4 = leaky_relu(l4)
        l4 = tf.nn.dropout(l4, 0.2)

        # Flatten it
        flat = tf.contrib.layers.flatten(relu3)
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)

    return out, logits

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [9]:
def generator(z, out_channel_dim, is_train=True):

    # TODO: Implement Function
    with tf.variable_scope("generator",reuse = not is_train):
        l1 = tf.layers.dense(z, 7*7*32)
        l1 = tf.layers.batch_normalization(l1, training=is_train)
        l1 = leaky_relu(l1)
        l1 = tf.layers.dropout(l1, dropout, training=is_train)
        l1 = tf.reshape(l1, (-1, 7, 7, 32))
     
        l2 = tf.layers.conv2d_transpose(l1, 16, 3, strides=2, padding='same')
        l2 = tf.layers.batch_normalization(l2, training=is_train)
        l2 = leaky_relu(l2)
        l2 = tf.layers.dropout(l2, dropout, training=is_train)
        
        l2 = tf.layers.conv2d_transpose(l2, 32, 5, strides=2, padding='same')
        l2 = tf.layers.batch_normalization(l2, training=is_train)
        l2 = leaky_relu(l2)
        l2 = tf.layers.dropout(l2, dropout, training=is_train)
        
        l2 = tf.layers.conv2d(l2, 64, 7, strides=1, padding='same')
        l2 = tf.layers.batch_normalization(l2, training=is_train)
        l2 = leaky_relu(l2)
        l2 = tf.layers.dropout(l2, dropout, training=is_train)
        
        #28*28
        l3 = tf.layers.conv2d(l2, 128, 4, strides=1, padding='same')
        l3 = tf.layers.batch_normalization(l3, training=is_train)
        l3 = leaky_relu(l3)
        l3 = tf.layers.dropout(l3, dropout, training=is_train)
        
        l4 = tf.layers.conv2d(l3, 256, 2, strides=1, padding='same')
        l4 = tf.layers.batch_normalization(l4, training=is_train)
        l4 = leaky_relu(l4)
        l4 = tf.layers.dropout(l4, dropout, training=is_train)
        
        l4 = tf.layers.conv2d(l4, 512, 1, strides=1, padding='same')
        l4 = leaky_relu(l4)
        l4 = tf.layers.dropout(l4, dropout, training=is_train)
        
        # Output layer
        logits = tf.layers.conv2d(l4, out_channel_dim, 1, strides=1, padding='same')
        # 28*28
        out = tf.tanh(logits)
    return out
      
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [10]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    
    # TODO: Implement Function
    g_model = generator(input_z, out_channel_dim, True)
    d_model_real, d_logits_real = discriminator(input_real, reuse=False)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)*0.9))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)*0.9))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)*0.9))

    d_loss = d_loss_real + d_loss_fake
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [11]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
        
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        # Optimize
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [12]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [13]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    print_every = 100
    
    # TODO: Build Model
    # The inputs
    image_mode = len(data_image_mode)# image channels. works because rgb is 3 characters long and l is one, the respective amount of image channels.
    input_images, input_z, l_rate = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)

    d_loss, g_loss = model_loss(input_images, input_z, data_shape[3])
    d_train, g_train = model_opt(d_loss, g_loss, l_rate, beta1)    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            steps = 0
            for batch_images in get_batches(batch_size):
                batch_images = batch_images * 2
                # TODO: Train Model
                steps += 1

                # Sample random noise for G
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                _ = sess.run(d_train, feed_dict={input_images: batch_images, input_z: batch_z, l_rate: learning_rate})
                _ = sess.run(g_train, feed_dict={input_images: batch_images, input_z: batch_z, l_rate: learning_rate})
                if steps % print_every == 0:
                    # At the end of each epoch, get the losses and print them out
                    train_loss_d = sess.run(d_loss, {input_z: batch_z, input_images: batch_images})
                    train_loss_g = g_loss.eval({input_z: batch_z, input_images: batch_images})

                    print("Epoch {}/{}...".format(epoch_i+1, epochs),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                    _ = show_generator_output(sess, 16, input_z, data_shape[3], data_image_mode)

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [14]:
batch_size = 16
z_dim = 100
learning_rate = 0.00025
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Discriminator Loss: 0.6045... Generator Loss: 2.0668
Epoch 1/2... Discriminator Loss: 0.7954... Generator Loss: 1.4761
Epoch 1/2... Discriminator Loss: 0.5955... Generator Loss: 2.1164
Epoch 1/2... Discriminator Loss: 0.8285... Generator Loss: 1.4361
Epoch 1/2... Discriminator Loss: 0.7261... Generator Loss: 1.5443
Epoch 1/2... Discriminator Loss: 0.9802... Generator Loss: 1.0528
Epoch 1/2... Discriminator Loss: 0.8126... Generator Loss: 1.1050
Epoch 1/2... Discriminator Loss: 0.7662... Generator Loss: 1.3151
Epoch 1/2... Discriminator Loss: 0.7223... Generator Loss: 1.8209
Epoch 1/2... Discriminator Loss: 0.7670... Generator Loss: 1.6161
Epoch 1/2... Discriminator Loss: 0.8125... Generator Loss: 2.0768
Epoch 1/2... Discriminator Loss: 1.0316... Generator Loss: 1.2781
Epoch 1/2... Discriminator Loss: 0.6675... Generator Loss: 1.8744
Epoch 1/2... Discriminator Loss: 0.7228... Generator Loss: 1.4642
Epoch 1/2... Discriminator Loss: 0.5864... Generator Loss: 1.6806
Epoch 1/2... Discriminator Loss: 0.8790... Generator Loss: 1.3635
Epoch 1/2... Discriminator Loss: 0.8692... Generator Loss: 1.6555
Epoch 1/2... Discriminator Loss: 0.7438... Generator Loss: 1.3875
Epoch 1/2... Discriminator Loss: 0.9656... Generator Loss: 1.2819
Epoch 1/2... Discriminator Loss: 0.8560... Generator Loss: 1.5574
Epoch 1/2... Discriminator Loss: 0.7885... Generator Loss: 1.8764
Epoch 1/2... Discriminator Loss: 0.8714... Generator Loss: 1.2818
Epoch 1/2... Discriminator Loss: 0.8756... Generator Loss: 1.3726
Epoch 1/2... Discriminator Loss: 0.7804... Generator Loss: 1.3300
Epoch 1/2... Discriminator Loss: 0.9450... Generator Loss: 1.1206
Epoch 1/2... Discriminator Loss: 0.7849... Generator Loss: 0.9908
Epoch 1/2... Discriminator Loss: 0.9359... Generator Loss: 0.9390
Epoch 1/2... Discriminator Loss: 0.9411... Generator Loss: 1.1273
Epoch 1/2... Discriminator Loss: 0.9015... Generator Loss: 1.6580
Epoch 1/2... Discriminator Loss: 0.8916... Generator Loss: 1.4470
Epoch 1/2... Discriminator Loss: 0.9452... Generator Loss: 1.3783
Epoch 1/2... Discriminator Loss: 0.7282... Generator Loss: 1.1003
Epoch 1/2... Discriminator Loss: 0.9665... Generator Loss: 1.2523
Epoch 1/2... Discriminator Loss: 1.2517... Generator Loss: 1.3687
Epoch 1/2... Discriminator Loss: 0.9430... Generator Loss: 1.2659
Epoch 1/2... Discriminator Loss: 0.9591... Generator Loss: 1.2876
Epoch 1/2... Discriminator Loss: 1.0474... Generator Loss: 1.2775
Epoch 2/2... Discriminator Loss: 0.8191... Generator Loss: 1.0851
Epoch 2/2... Discriminator Loss: 0.9410... Generator Loss: 1.0349
Epoch 2/2... Discriminator Loss: 1.1509... Generator Loss: 1.3947
Epoch 2/2... Discriminator Loss: 0.9292... Generator Loss: 1.3644
Epoch 2/2... Discriminator Loss: 0.9017... Generator Loss: 1.1656
Epoch 2/2... Discriminator Loss: 1.1082... Generator Loss: 1.1338
Epoch 2/2... Discriminator Loss: 0.8359... Generator Loss: 1.2002
Epoch 2/2... Discriminator Loss: 0.9733... Generator Loss: 0.9557
Epoch 2/2... Discriminator Loss: 1.1153... Generator Loss: 1.1675
Epoch 2/2... Discriminator Loss: 0.9537... Generator Loss: 1.4616
Epoch 2/2... Discriminator Loss: 0.9700... Generator Loss: 1.2366
Epoch 2/2... Discriminator Loss: 0.9110... Generator Loss: 1.3715
Epoch 2/2... Discriminator Loss: 0.8968... Generator Loss: 1.4915
Epoch 2/2... Discriminator Loss: 0.7105... Generator Loss: 1.3871
Epoch 2/2... Discriminator Loss: 0.8951... Generator Loss: 1.1367
Epoch 2/2... Discriminator Loss: 0.7882... Generator Loss: 1.5763
Epoch 2/2... Discriminator Loss: 1.0035... Generator Loss: 1.1166
Epoch 2/2... Discriminator Loss: 1.0920... Generator Loss: 1.4076
Epoch 2/2... Discriminator Loss: 0.9997... Generator Loss: 1.7041
Epoch 2/2... Discriminator Loss: 1.0991... Generator Loss: 1.1008
Epoch 2/2... Discriminator Loss: 0.9182... Generator Loss: 0.9135
Epoch 2/2... Discriminator Loss: 1.0354... Generator Loss: 1.3361
Epoch 2/2... Discriminator Loss: 0.9241... Generator Loss: 0.9957
Epoch 2/2... Discriminator Loss: 0.9340... Generator Loss: 1.2330
Epoch 2/2... Discriminator Loss: 1.1404... Generator Loss: 1.0821
Epoch 2/2... Discriminator Loss: 0.8287... Generator Loss: 1.2009
Epoch 2/2... Discriminator Loss: 1.0833... Generator Loss: 0.8797
Epoch 2/2... Discriminator Loss: 0.8711... Generator Loss: 1.0913
Epoch 2/2... Discriminator Loss: 1.0238... Generator Loss: 0.9810
Epoch 2/2... Discriminator Loss: 1.0930... Generator Loss: 1.0706
Epoch 2/2... Discriminator Loss: 0.9367... Generator Loss: 1.3390
Epoch 2/2... Discriminator Loss: 0.8662... Generator Loss: 1.2612
Epoch 2/2... Discriminator Loss: 0.9170... Generator Loss: 1.1780
Epoch 2/2... Discriminator Loss: 1.2815... Generator Loss: 1.0513
Epoch 2/2... Discriminator Loss: 1.1445... Generator Loss: 1.1222
Epoch 2/2... Discriminator Loss: 0.9592... Generator Loss: 1.1597
Epoch 2/2... Discriminator Loss: 0.9114... Generator Loss: 1.5410

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [15]:
batch_size = 16
z_dim = 100
learning_rate = 0.00025
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Discriminator Loss: 0.4150... Generator Loss: 2.4994
Epoch 1/1... Discriminator Loss: 0.3992... Generator Loss: 3.3299
Epoch 1/1... Discriminator Loss: 0.3735... Generator Loss: 4.0044
Epoch 1/1... Discriminator Loss: 0.3687... Generator Loss: 3.6995
Epoch 1/1... Discriminator Loss: 0.3716... Generator Loss: 4.0660
Epoch 1/1... Discriminator Loss: 0.3873... Generator Loss: 3.4670
Epoch 1/1... Discriminator Loss: 0.4109... Generator Loss: 3.2982
Epoch 1/1... Discriminator Loss: 0.3780... Generator Loss: 3.5939
Epoch 1/1... Discriminator Loss: 0.4019... Generator Loss: 3.7142
Epoch 1/1... Discriminator Loss: 0.3954... Generator Loss: 2.9193
Epoch 1/1... Discriminator Loss: 0.4011... Generator Loss: 3.0064
Epoch 1/1... Discriminator Loss: 0.3866... Generator Loss: 3.1769
Epoch 1/1... Discriminator Loss: 0.4136... Generator Loss: 4.1096
Epoch 1/1... Discriminator Loss: 0.3833... Generator Loss: 2.5406
Epoch 1/1... Discriminator Loss: 0.3974... Generator Loss: 3.0201
Epoch 1/1... Discriminator Loss: 0.3911... Generator Loss: 3.7495
Epoch 1/1... Discriminator Loss: 0.4475... Generator Loss: 2.8036
Epoch 1/1... Discriminator Loss: 0.4278... Generator Loss: 2.8203
Epoch 1/1... Discriminator Loss: 0.4110... Generator Loss: 2.3549
Epoch 1/1... Discriminator Loss: 0.3942... Generator Loss: 3.0258
Epoch 1/1... Discriminator Loss: 0.4994... Generator Loss: 2.4566
Epoch 1/1... Discriminator Loss: 0.4237... Generator Loss: 3.0918
Epoch 1/1... Discriminator Loss: 0.4389... Generator Loss: 2.8167
Epoch 1/1... Discriminator Loss: 0.4061... Generator Loss: 2.1836
Epoch 1/1... Discriminator Loss: 0.4835... Generator Loss: 2.5249
Epoch 1/1... Discriminator Loss: 0.4484... Generator Loss: 2.3975
Epoch 1/1... Discriminator Loss: 0.4011... Generator Loss: 2.9457
Epoch 1/1... Discriminator Loss: 0.4612... Generator Loss: 2.8462
Epoch 1/1... Discriminator Loss: 0.6716... Generator Loss: 2.6143
Epoch 1/1... Discriminator Loss: 0.3912... Generator Loss: 3.5968
Epoch 1/1... Discriminator Loss: 0.4679... Generator Loss: 2.3340
Epoch 1/1... Discriminator Loss: 0.4315... Generator Loss: 2.6434
Epoch 1/1... Discriminator Loss: 0.4734... Generator Loss: 2.4967
Epoch 1/1... Discriminator Loss: 0.4145... Generator Loss: 2.6934
Epoch 1/1... Discriminator Loss: 0.4293... Generator Loss: 2.7693
Epoch 1/1... Discriminator Loss: 0.3867... Generator Loss: 3.6028
Epoch 1/1... Discriminator Loss: 0.3847... Generator Loss: 3.3817
Epoch 1/1... Discriminator Loss: 0.6521... Generator Loss: 1.8668
Epoch 1/1... Discriminator Loss: 0.5289... Generator Loss: 2.5935
Epoch 1/1... Discriminator Loss: 0.3846... Generator Loss: 3.6053
Epoch 1/1... Discriminator Loss: 0.4634... Generator Loss: 2.4083
Epoch 1/1... Discriminator Loss: 0.4977... Generator Loss: 2.4174
Epoch 1/1... Discriminator Loss: 0.4507... Generator Loss: 2.5498
Epoch 1/1... Discriminator Loss: 0.3904... Generator Loss: 3.2573
Epoch 1/1... Discriminator Loss: 0.4440... Generator Loss: 2.6267
Epoch 1/1... Discriminator Loss: 0.4352... Generator Loss: 2.5614
Epoch 1/1... Discriminator Loss: 0.4934... Generator Loss: 1.5391
Epoch 1/1... Discriminator Loss: 0.4839... Generator Loss: 2.2668
Epoch 1/1... Discriminator Loss: 0.4067... Generator Loss: 2.6452
Epoch 1/1... Discriminator Loss: 0.4232... Generator Loss: 3.3820
Epoch 1/1... Discriminator Loss: 0.4445... Generator Loss: 2.7387
Epoch 1/1... Discriminator Loss: 0.3801... Generator Loss: 3.1592
Epoch 1/1... Discriminator Loss: 0.6522... Generator Loss: 3.2800
Epoch 1/1... Discriminator Loss: 0.4118... Generator Loss: 3.0442
Epoch 1/1... Discriminator Loss: 0.4912... Generator Loss: 2.4749
Epoch 1/1... Discriminator Loss: 0.4371... Generator Loss: 2.3868
Epoch 1/1... Discriminator Loss: 0.5201... Generator Loss: 3.1201
Epoch 1/1... Discriminator Loss: 0.4101... Generator Loss: 3.2627
Epoch 1/1... Discriminator Loss: 0.3825... Generator Loss: 3.9124
Epoch 1/1... Discriminator Loss: 0.4441... Generator Loss: 2.6017
Epoch 1/1... Discriminator Loss: 0.4235... Generator Loss: 3.5778
Epoch 1/1... Discriminator Loss: 0.4657... Generator Loss: 2.7367
Epoch 1/1... Discriminator Loss: 0.3915... Generator Loss: 3.7436
Epoch 1/1... Discriminator Loss: 0.4226... Generator Loss: 2.7437
Epoch 1/1... Discriminator Loss: 0.4568... Generator Loss: 2.0461
Epoch 1/1... Discriminator Loss: 0.4518... Generator Loss: 2.3273
Epoch 1/1... Discriminator Loss: 0.4096... Generator Loss: 3.5290
Epoch 1/1... Discriminator Loss: 0.4010... Generator Loss: 3.7885
Epoch 1/1... Discriminator Loss: 0.4878... Generator Loss: 2.9998
Epoch 1/1... Discriminator Loss: 0.3680... Generator Loss: 3.2291
Epoch 1/1... Discriminator Loss: 0.3734... Generator Loss: 4.0705
Epoch 1/1... Discriminator Loss: 0.5297... Generator Loss: 2.2437
Epoch 1/1... Discriminator Loss: 0.4517... Generator Loss: 3.0631
Epoch 1/1... Discriminator Loss: 0.5822... Generator Loss: 2.6695
Epoch 1/1... Discriminator Loss: 0.3654... Generator Loss: 3.8434
Epoch 1/1... Discriminator Loss: 0.4053... Generator Loss: 3.1503
Epoch 1/1... Discriminator Loss: 0.3915... Generator Loss: 3.0615
Epoch 1/1... Discriminator Loss: 0.4565... Generator Loss: 2.2541
Epoch 1/1... Discriminator Loss: 0.3675... Generator Loss: 3.8558
Epoch 1/1... Discriminator Loss: 0.4173... Generator Loss: 3.2093
Epoch 1/1... Discriminator Loss: 0.4246... Generator Loss: 3.5712
Epoch 1/1... Discriminator Loss: 0.3949... Generator Loss: 3.7166
Epoch 1/1... Discriminator Loss: 0.4632... Generator Loss: 2.2241
Epoch 1/1... Discriminator Loss: 0.3866... Generator Loss: 3.2164
Epoch 1/1... Discriminator Loss: 0.3674... Generator Loss: 3.1635
Epoch 1/1... Discriminator Loss: 0.4079... Generator Loss: 2.4319
Epoch 1/1... Discriminator Loss: 0.3804... Generator Loss: 2.5979
Epoch 1/1... Discriminator Loss: 0.6261... Generator Loss: 1.5783
Epoch 1/1... Discriminator Loss: 0.4067... Generator Loss: 2.8116
Epoch 1/1... Discriminator Loss: 0.4050... Generator Loss: 3.5230
Epoch 1/1... Discriminator Loss: 0.3917... Generator Loss: 3.0258
Epoch 1/1... Discriminator Loss: 0.5206... Generator Loss: 3.0535
Epoch 1/1... Discriminator Loss: 0.3972... Generator Loss: 2.8841
Epoch 1/1... Discriminator Loss: 0.4193... Generator Loss: 2.5440
Epoch 1/1... Discriminator Loss: 0.3910... Generator Loss: 3.7870
Epoch 1/1... Discriminator Loss: 0.3978... Generator Loss: 4.1608
Epoch 1/1... Discriminator Loss: 0.4775... Generator Loss: 2.6334
Epoch 1/1... Discriminator Loss: 0.3771... Generator Loss: 3.2786
Epoch 1/1... Discriminator Loss: 0.3715... Generator Loss: 3.4718
Epoch 1/1... Discriminator Loss: 0.3580... Generator Loss: 4.0282
Epoch 1/1... Discriminator Loss: 0.4078... Generator Loss: 3.7618
Epoch 1/1... Discriminator Loss: 0.3464... Generator Loss: 3.9024
Epoch 1/1... Discriminator Loss: 0.3690... Generator Loss: 3.4664
Epoch 1/1... Discriminator Loss: 0.3721... Generator Loss: 3.2884
Epoch 1/1... Discriminator Loss: 0.3977... Generator Loss: 3.3379
Epoch 1/1... Discriminator Loss: 0.3822... Generator Loss: 3.3985
Epoch 1/1... Discriminator Loss: 0.3713... Generator Loss: 2.8247
Epoch 1/1... Discriminator Loss: 0.4130... Generator Loss: 2.4340
Epoch 1/1... Discriminator Loss: 0.5871... Generator Loss: 3.4815
Epoch 1/1... Discriminator Loss: 0.4024... Generator Loss: 3.8906
Epoch 1/1... Discriminator Loss: 0.3572... Generator Loss: 4.0181
Epoch 1/1... Discriminator Loss: 0.4294... Generator Loss: 2.8168
Epoch 1/1... Discriminator Loss: 0.3807... Generator Loss: 3.0924
Epoch 1/1... Discriminator Loss: 0.4144... Generator Loss: 2.8783
Epoch 1/1... Discriminator Loss: 0.3795... Generator Loss: 3.1651
Epoch 1/1... Discriminator Loss: 0.3927... Generator Loss: 3.2202
Epoch 1/1... Discriminator Loss: 0.3918... Generator Loss: 3.9187
Epoch 1/1... Discriminator Loss: 0.3806... Generator Loss: 3.4664
Epoch 1/1... Discriminator Loss: 0.3864... Generator Loss: 3.0241
Epoch 1/1... Discriminator Loss: 0.3922... Generator Loss: 3.9526
Epoch 1/1... Discriminator Loss: 0.3570... Generator Loss: 3.7996
Epoch 1/1... Discriminator Loss: 0.3581... Generator Loss: 3.5795
Epoch 1/1... Discriminator Loss: 0.3560... Generator Loss: 3.6878
Epoch 1/1... Discriminator Loss: 0.3848... Generator Loss: 2.8690
Epoch 1/1... Discriminator Loss: 0.3608... Generator Loss: 3.7854
Epoch 1/1... Discriminator Loss: 0.3631... Generator Loss: 4.0298

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.